scholarly journals Spatiotemporally Resolved Multivariate Pattern Analysis for M/EEG

2021 ◽  
Author(s):  
Cameron J Higgins ◽  
Diego Vidaurre ◽  
Nils Kolling ◽  
Yunzhe Liu ◽  
Tim Behrens ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Predictive Accuracy ◽  
Brain Activity ◽  
Activity Patterns ◽  
Multivariate Pattern Analysis ◽  
Model Framework ◽  
Model Inversion ◽  
Classification And Regression ◽  
Multivariate Pattern ◽  
Generative Encoding

An emerging goal in neuroscience is tracking what information is represented in brain activity over time as a participant completes some task. Whilst EEG and MEG offer millisecond temporal resolution of how activity patterns emerge and evolve, standard decoding methods present significant barriers to interpretability as they obscure the underlying spatial and temporal activity patterns. We instead propose the use of a generative encoding model framework that simultaneously infers the multivariate spatial patterns of activity and the variable timing at which these patterns emerge on individual trials. An encoding model inversion allows predictions to be made about unseen test data in the same way as in standard decoding methodology. These SpatioTemporally Resolved MVPA (STRM) models can be flexibly applied to a wide variety of experimental paradigms, including classification and regression tasks. We show that these models provide insightful maps of the activity driving predictive accuracy metrics; demonstrate behaviourally meaningful variation in the timing of pattern emergence on individual trials; and achieve predictive accuracies that are either equivalent or surpass those achieved by more widely used methods. This provides a new avenue for investigating the brain's representational dynamics and could ultimately support more flexible experimental designs in future.

scholarly journals Decoding Digits and Dice with Magnetoencephalography: Evidence for a Shared Representation of Magnitude

2018 ◽  
Vol 30 (7) ◽  
pp. 999-1010 ◽  
Author(s):  
Lina Teichmann ◽  
Tijl Grootswagers ◽  
Thomas Carlson ◽  
Anina N. Rich
Keyword(s):  
Time Course ◽  
Numerical Cognition ◽  
Pattern Analysis ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Multivariate Pattern Analysis ◽  
Ongoing Debate ◽  
Activation Patterns ◽  
Magnitude Processing ◽  
Multivariate Pattern

Numerical format describes the way magnitude is conveyed, for example, as a digit (“3”) or Roman numeral (“III”). In the field of numerical cognition, there is an ongoing debate of whether magnitude representation is independent of numerical format. Here, we examine the time course of magnitude processing when using different symbolic formats. We presented participants with a series of digits and dice patterns corresponding to the magnitudes of 1 to 6 while performing a 1-back task on magnitude. Magnetoencephalography offers an opportunity to record brain activity with high temporal resolution. Multivariate pattern analysis applied to magnetoencephalographic data allows us to draw conclusions about brain activation patterns underlying information processing over time. The results show that we can cross-decode magnitude when training the classifier on magnitude presented in one symbolic format and testing the classifier on the other symbolic format. This suggests a similar representation of these numerical symbols. In addition, results from a time generalization analysis show that digits were accessed slightly earlier than dice, demonstrating temporal asynchronies in their shared representation of magnitude. Together, our methods allow a distinction between format-specific signals and format-independent representations of magnitude showing evidence that there is a shared representation of magnitude accessed via different symbols.

scholarly journals Neural correlates of conscious visual perception lie outside the visual system: evidence from the double-drift illusion

2019 ◽  
Author(s):  
Sirui Liu ◽  
Qing Yu ◽  
Peter U. Tse ◽  
Patrick Cavanagh
Keyword(s):  
Visual System ◽  
Visual Processing ◽  
Pattern Analysis ◽  
Activity Patterns ◽  
Neural Correlates ◽  
Multivariate Pattern Analysis ◽  
Visual Areas ◽  
Visual Cortices ◽  
Multivariate Pattern ◽  
The Brain

SummaryWhen perception differs from the physical stimulus, as it does for visual illusions and binocular rivalry, the opportunity arises to localize where perception emerges in the visual processing hierarchy. Representations prior to that stage differ from the eventual conscious percept even though they provide input to it. Here we investigate where and how a remarkable misperception of position emerges in the brain. This “double-drift” illusion causes a dramatic mismatch between retinal and perceived location, producing a perceived path that can differ from its physical path by 45° or more [1]. The deviations in the perceived trajectory can accumulate over at least a second [1] whereas other motion-induced position shifts accumulate over only 80 to 100 ms before saturating [2]. Using fMRI and multivariate pattern analysis, we find that the illusory path does not share activity patterns with a matched physical path in any early visual areas. In contrast, a whole-brain searchlight analysis reveals a shared representation in more anterior regions of the brain. These higher-order areas would have the longer time constants required to accumulate the small moment-to-moment position offsets that presumably originate in early visual cortices, and then transform these sensory inputs into a final conscious percept. The dissociation between perception and the activity in early sensory cortex suggests that perceived position does not emerge in what is traditionally regarded as the visual system but emerges instead at a much higher level.

scholarly journals Skill learning strengthens cortical representations of motor sequences

eLife ◽  
2013 ◽  
Vol 2 ◽  
Author(s):  
Tobias Wiestler ◽  
Jörn Diedrichsen
Keyword(s):  
Skill Acquisition ◽  
Motor Skill ◽  
Brain Activity ◽  
Activity Patterns ◽  
Skill Learning ◽  
Multivariate Pattern Analysis ◽  
Motor Skill Acquisition ◽  
Average Activity ◽  
Multivariate Pattern ◽  
Cortical Representations

Motor-skill learning can be accompanied by both increases and decreases in brain activity. Increases may indicate neural recruitment, while decreases may imply that a region became unimportant or developed a more efficient representation of the skill. These overlapping mechanisms make interpreting learning-related changes of spatially averaged activity difficult. Here we show that motor-skill acquisition is associated with the emergence of highly distinguishable activity patterns for trained movement sequences, in the absence of average activity increases. During functional magnetic resonance imaging, participants produced either four trained or four untrained finger sequences. Using multivariate pattern analysis, both untrained and trained sequences could be discriminated in primary and secondary motor areas. However, trained sequences were classified more reliably, especially in the supplementary motor area. Our results indicate skill learning leads to the development of specialized neuronal circuits, which allow the execution of fast and accurate sequential movements without average increases in brain activity.

scholarly journals Decoding digits and dice with Magnetoencephalography: Evidence for a shared representation of magnitude

2018 ◽  
Author(s):  
A. Lina Teichmann ◽  
Tijl Grootswagers ◽  
Thomas Carlson ◽  
Anina N. Rich
Keyword(s):  
Time Course ◽  
Numerical Cognition ◽  
Pattern Analysis ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Multivariate Pattern Analysis ◽  
Ongoing Debate ◽  
Activation Patterns ◽  
Magnitude Processing ◽  
Multivariate Pattern

AbstractNumerical format describes the way magnitude is conveyed, for example as a digit (‘3’) or Roman Numeral (‘III’). In the field of numerical cognition, there is an ongoing debate of whether magnitude representation is independent of numerical format. Here, we examine the time course of magnitude processing when using different symbolic formats. We presented participants with a series of digits and dice patterns corresponding to the magnitudes of 1 to 6 while performing a 1-back task on magnitude. Magnetoencephalography (MEG) offers an opportunity to record brain activity with high temporal resolution. Multivariate Pattern Analysis (MVPA) applied to MEG data allows us to draw conclusions about brain activation patterns underlying information processing over time. The results show that we can crossdecode magnitude when training the classifier on magnitude presented in one symbolic format and testing the classifier on the other symbolic format. This suggests similar representation of these numerical symbols. Additionally, results from a time-generalisation analysis show that digits were accessed slightly earlier than dice, demonstrating temporal asynchronies in their shared representation of magnitude. Together, our methods allow a distinction between format-specific signals and format-independent representations of magnitude showing evidence that there is a shared representation of magnitude accessed via different symbols.

scholarly journals GLMdenoise improves multivariate pattern analysis of fMRI data

2018 ◽  
Author(s):  
Ian Charest ◽  
Nikolaus Kriegeskorte ◽  
Kendrick N. Kay
Keyword(s):  
Classification Accuracy ◽  
Pattern Analysis ◽  
Activity Patterns ◽  
Experimental Studies ◽  
Fmri Data ◽  
Correlated Noise ◽  
Multivariate Pattern Analysis ◽  
Spatially Correlated ◽  
Multivariate Pattern ◽  
Practical Impact

ABSTRACTGLMdenoise is a denoising technique for task-based fMRI. In GLMdenoise, estimates of spatially correlated noise (which may be physiological, instrumental, motion-related, or neural in origin) are derived from the data and incorporated as nuisance regressors in a general linear model (GLM) analysis. We previously showed that GLMdenoise outperforms a variety of other denoising techniques in terms of cross-validation accuracy of GLM estimates (Kay et al., 2013a). However, the practical impact of denoising for experimental studies remains unclear. Here we examine whether and to what extent GLMdenoise improves sensitivity in the context of multivariate pattern analysis of fMRI data. On a large number of participants (31 participants across 4 experiments; 3 T, gradient-echo, spatial resolution 2–3.75 mm, temporal resolution 1.3–2 s, number of conditions 32–75), we perform representational similarity analysis (Kriegeskorte et al., 2008a) as well as pattern classification (Haxby et al., 2001). We find that GLMdenoise substantially improves replicability of representational dissimilarity matrices (RDMs) across independent splits of each participant’s dataset (average RDM replicability increases from r = 0.46 to r = 0.61). Additionally, we find that GLMdenoise substantially improves pairwise classification accuracy (average classification accuracy increases from 79% correct to 84% correct). We show that GLMdenoise often improves and never degrades performance for individual participants and that GLMdenoise also improves across-participant consistency. We conclude that GLMdenoise is a useful tool that can be routinely used to maximize the amount of information extracted from fMRI activity patterns.

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